Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10127
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric15
Categorical6

Alerts

Faixa Salarial Anual is highly overall correlated with SexoHigh correlation
Idade is highly overall correlated with Meses como ClienteHigh correlation
Limite is highly overall correlated with Limite DisponívelHigh correlation
Limite Consumido is highly overall correlated with Taxa de Utilização CartãoHigh correlation
Limite Disponível is highly overall correlated with Limite and 1 other fieldsHigh correlation
Meses como Cliente is highly overall correlated with IdadeHigh correlation
Qtde Transacoes 12m is highly overall correlated with Valor Transacoes 12mHigh correlation
Sexo is highly overall correlated with Faixa Salarial AnualHigh correlation
Taxa de Utilização Cartão is highly overall correlated with Limite Consumido and 1 other fieldsHigh correlation
Valor Transacoes 12m is highly overall correlated with Qtde Transacoes 12mHigh correlation
Categoria Cartão is highly imbalanced (79.2%)Imbalance
CLIENTNUM has unique valuesUnique
Dependentes has 904 (8.9%) zerosZeros
Contatos 12m has 399 (3.9%) zerosZeros
Limite Consumido has 2470 (24.4%) zerosZeros
Taxa de Utilização Cartão has 2470 (24.4%) zerosZeros

Reproduction

Analysis started2024-10-04 14:01:52.285246
Analysis finished2024-10-04 14:03:07.706350
Duration1 minute and 15.42 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:08.674401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2024-10-04T14:03:09.180730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

Categoria
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Cliente
8500 
Cancelado
1627 

Length

Max length9
Median length7
Mean length7.3213192
Min length7

Characters and Unicode

Total characters74143
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCliente
2nd rowCliente
3rd rowCliente
4th rowCliente
5th rowCliente

Common Values

ValueCountFrequency (%)
Cliente 8500
83.9%
Cancelado 1627
 
16.1%

Length

2024-10-04T14:03:09.479904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:09.811159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cliente 8500
83.9%
cancelado 1627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
e 18627
25.1%
C 10127
13.7%
l 10127
13.7%
n 10127
13.7%
i 8500
11.5%
t 8500
11.5%
a 3254
 
4.4%
c 1627
 
2.2%
d 1627
 
2.2%
o 1627
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18627
25.1%
C 10127
13.7%
l 10127
13.7%
n 10127
13.7%
i 8500
11.5%
t 8500
11.5%
a 3254
 
4.4%
c 1627
 
2.2%
d 1627
 
2.2%
o 1627
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18627
25.1%
C 10127
13.7%
l 10127
13.7%
n 10127
13.7%
i 8500
11.5%
t 8500
11.5%
a 3254
 
4.4%
c 1627
 
2.2%
d 1627
 
2.2%
o 1627
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18627
25.1%
C 10127
13.7%
l 10127
13.7%
n 10127
13.7%
i 8500
11.5%
t 8500
11.5%
a 3254
 
4.4%
c 1627
 
2.2%
d 1627
 
2.2%
o 1627
 
2.2%

Idade
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:10.056617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2024-10-04T14:03:10.350821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

Sexo
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2024-10-04T14:03:10.648169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:10.879586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Dependentes
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:11.067287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2024-10-04T14:03:11.288713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

Educação
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Ensino Superior
3128 
Ensino Médio
2013 
Não informado
1519 
Sem ensino formal
1487 
Ensino Superior Incompleto
1013 
Other values (2)
967 

Length

Max length26
Median length20
Mean length15.485237
Min length9

Characters and Unicode

Total characters156819
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnsino Médio
2nd rowEnsino Superior
3rd rowEnsino Superior
4th rowEnsino Médio
5th rowSem ensino formal

Common Values

ValueCountFrequency (%)
Ensino Superior 3128
30.9%
Ensino Médio 2013
19.9%
Não informado 1519
15.0%
Sem ensino formal 1487
14.7%
Ensino Superior Incompleto 1013
 
10.0%
Post-Ensino Superior 516
 
5.1%
Doutorado 451
 
4.5%

Length

2024-10-04T14:03:11.561562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:11.886135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ensino 7641
34.3%
superior 4657
20.9%
médio 2013
 
9.0%
não 1519
 
6.8%
informado 1519
 
6.8%
sem 1487
 
6.7%
formal 1487
 
6.7%
incompleto 1013
 
4.5%
post-ensino 516
 
2.3%
doutorado 451
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 24766
15.8%
n 18846
12.0%
i 16346
10.4%
r 12771
 
8.1%
12176
 
7.8%
s 8673
 
5.5%
e 8644
 
5.5%
E 6670
 
4.3%
S 6144
 
3.9%
p 5670
 
3.6%
Other values (16) 36113
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 24766
15.8%
n 18846
12.0%
i 16346
10.4%
r 12771
 
8.1%
12176
 
7.8%
s 8673
 
5.5%
e 8644
 
5.5%
E 6670
 
4.3%
S 6144
 
3.9%
p 5670
 
3.6%
Other values (16) 36113
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 24766
15.8%
n 18846
12.0%
i 16346
10.4%
r 12771
 
8.1%
12176
 
7.8%
s 8673
 
5.5%
e 8644
 
5.5%
E 6670
 
4.3%
S 6144
 
3.9%
p 5670
 
3.6%
Other values (16) 36113
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 24766
15.8%
n 18846
12.0%
i 16346
10.4%
r 12771
 
8.1%
12176
 
7.8%
s 8673
 
5.5%
e 8644
 
5.5%
E 6670
 
4.3%
S 6144
 
3.9%
p 5670
 
3.6%
Other values (16) 36113
23.0%

Estado Civil
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Casado
4687 
Solteiro
3943 
Não informado
749 
Divorciado
748 

Length

Max length13
Median length10
Mean length7.5918831
Min length6

Characters and Unicode

Total characters76883
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasado
2nd rowSolteiro
3rd rowCasado
4th rowNão informado
5th rowCasado

Common Values

ValueCountFrequency (%)
Casado 4687
46.3%
Solteiro 3943
38.9%
Não informado 749
 
7.4%
Divorciado 748
 
7.4%

Length

2024-10-04T14:03:12.178401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:12.441140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
casado 4687
43.1%
solteiro 3943
36.3%
não 749
 
6.9%
informado 749
 
6.9%
divorciado 748
 
6.9%

Most occurring characters

ValueCountFrequency (%)
o 16316
21.2%
a 10871
14.1%
i 6188
 
8.0%
d 6184
 
8.0%
r 5440
 
7.1%
C 4687
 
6.1%
s 4687
 
6.1%
l 3943
 
5.1%
t 3943
 
5.1%
e 3943
 
5.1%
Other values (10) 10681
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76883
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 16316
21.2%
a 10871
14.1%
i 6188
 
8.0%
d 6184
 
8.0%
r 5440
 
7.1%
C 4687
 
6.1%
s 4687
 
6.1%
l 3943
 
5.1%
t 3943
 
5.1%
e 3943
 
5.1%
Other values (10) 10681
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76883
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 16316
21.2%
a 10871
14.1%
i 6188
 
8.0%
d 6184
 
8.0%
r 5440
 
7.1%
C 4687
 
6.1%
s 4687
 
6.1%
l 3943
 
5.1%
t 3943
 
5.1%
e 3943
 
5.1%
Other values (10) 10681
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76883
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 16316
21.2%
a 10871
14.1%
i 6188
 
8.0%
d 6184
 
8.0%
r 5440
 
7.1%
C 4687
 
6.1%
s 4687
 
6.1%
l 3943
 
5.1%
t 3943
 
5.1%
e 3943
 
5.1%
Other values (10) 10681
13.9%

Faixa Salarial Anual
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
Não informado
1112 

Length

Max length14
Median length13
Mean length12.138936
Min length7

Characters and Unicode

Total characters122931
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowLess than $40K
3rd row$80K - $120K
4th rowLess than $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
Less than $40K 3561
35.2%
$40K - $60K 1790
17.7%
$80K - $120K 1535
15.2%
$60K - $80K 1402
 
13.8%
Não informado 1112
 
11.0%
$120K + 727
 
7.2%

Length

2024-10-04T14:03:12.683185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:13.004880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.1%
40k 5351
18.7%
less 3561
12.5%
than 3561
12.5%
60k 3192
11.2%
80k 2937
10.3%
120k 2262
7.9%
não 1112
 
3.9%
informado 1112
 
3.9%

Most occurring characters

ValueCountFrequency (%)
18415
15.0%
$ 13742
11.2%
0 13742
11.2%
K 13742
11.2%
s 7122
 
5.8%
4 5351
 
4.4%
- 4727
 
3.8%
a 4673
 
3.8%
n 4673
 
3.8%
L 3561
 
2.9%
Other values (16) 33183
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18415
15.0%
$ 13742
11.2%
0 13742
11.2%
K 13742
11.2%
s 7122
 
5.8%
4 5351
 
4.4%
- 4727
 
3.8%
a 4673
 
3.8%
n 4673
 
3.8%
L 3561
 
2.9%
Other values (16) 33183
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18415
15.0%
$ 13742
11.2%
0 13742
11.2%
K 13742
11.2%
s 7122
 
5.8%
4 5351
 
4.4%
- 4727
 
3.8%
a 4673
 
3.8%
n 4673
 
3.8%
L 3561
 
2.9%
Other values (16) 33183
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18415
15.0%
$ 13742
11.2%
0 13742
11.2%
K 13742
11.2%
s 7122
 
5.8%
4 5351
 
4.4%
- 4727
 
3.8%
a 4673
 
3.8%
n 4673
 
3.8%
L 3561
 
2.9%
Other values (16) 33183
27.0%

Categoria Cartão
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size79.2 KiB
Blue
9435 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175193
Min length4

Characters and Unicode

Total characters41694
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9435
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%
(Missing) 1
 
< 0.1%

Length

2024-10-04T14:03:13.308091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-04T14:03:13.594275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
blue 9435
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10126
24.3%
e 9990
24.0%
u 9455
22.7%
B 9435
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 10126
24.3%
e 9990
24.0%
u 9455
22.7%
B 9435
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 10126
24.3%
e 9990
24.0%
u 9455
22.7%
B 9435
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 10126
24.3%
e 9990
24.0%
u 9455
22.7%
B 9435
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Meses como Cliente
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:13.856084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2024-10-04T14:03:14.122029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

Produtos Contratados
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:14.351923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2024-10-04T14:03:14.561427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

Inatividade 12m
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:14.777211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2024-10-04T14:03:15.002561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

Contatos 12m
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:15.234396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2024-10-04T14:03:15.459780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

Limite
Real number (ℝ)

HIGH CORRELATION 

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.9537
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:15.752016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.7767
Coefficient of variation (CV)1.0529223
Kurtosis1.8089893
Mean8631.9537
Median Absolute Deviation (MAD)2593
Skewness1.6667258
Sum87415795
Variance82605861
MonotonicityNot monotonic
2024-10-04T14:03:16.060197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 508
 
5.0%
1438.3 507
 
5.0%
9959 18
 
0.2%
15987 18
 
0.2%
23981 12
 
0.1%
2490 11
 
0.1%
6224 11
 
0.1%
3735 11
 
0.1%
7469 10
 
0.1%
2069 8
 
0.1%
Other values (6195) 9013
89.0%
ValueCountFrequency (%)
1438.3 507
5.0%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
34516 508
5.0%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34140 1
 
< 0.1%
34058 1
 
< 0.1%
34010 1
 
< 0.1%

Limite Consumido
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:16.353802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2024-10-04T14:03:16.652514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1965 12
 
0.1%
1480 12
 
0.1%
1434 11
 
0.1%
1664 11
 
0.1%
1720 11
 
0.1%
1590 10
 
0.1%
1542 10
 
0.1%
1528 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

Limite Disponível
Real number (ℝ)

HIGH CORRELATION 

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:16.955838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2024-10-04T14:03:17.263808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
701 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
463 7
 
0.1%
990 6
 
0.1%
788 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%
Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.81727
Minimum0
Maximum3397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:17.620144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1078
Maximum3397
Range3397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation354.05036
Coefficient of variation (CV)3.2536228
Kurtosis10.074284
Mean108.81727
Median Absolute Deviation (MAD)0.114
Skewness3.2393788
Sum1101992.5
Variance125351.66
MonotonicityNot monotonic
2024-10-04T14:03:17.932983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.699 32
 
0.3%
0.722 32
 
0.3%
0.731 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3397 1
< 0.1%
3355 1
< 0.1%
2675 1
< 0.1%
2594 1
< 0.1%
2368 1
< 0.1%
2357 1
< 0.1%
2316 1
< 0.1%
2282 1
< 0.1%
2275 1
< 0.1%
2271 1
< 0.1%

Valor Transacoes 12m
Real number (ℝ)

HIGH CORRELATION 

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.0863
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:18.234771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.1293
Coefficient of variation (CV)0.77135847
Kurtosis3.8940234
Mean4404.0863
Median Absolute Deviation (MAD)1308
Skewness2.0410034
Sum44600182
Variance11540487
MonotonicityNot monotonic
2024-10-04T14:03:18.527878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 11
 
0.1%
4518 10
 
0.1%
2229 10
 
0.1%
4220 9
 
0.1%
4869 9
 
0.1%
4037 9
 
0.1%
4313 9
 
0.1%
4498 9
 
0.1%
4042 9
 
0.1%
Other values (5023) 10031
99.1%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Qtde Transacoes 12m
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:18.833980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2024-10-04T14:03:19.195817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%
Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.573618
Minimum0
Maximum3714
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:19.715159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1029
Maximum3714
Range3714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation283.98806
Coefficient of variation (CV)4.2657748
Kurtosis22.303029
Mean66.573618
Median Absolute Deviation (MAD)0.119
Skewness4.5204983
Sum674191.03
Variance80649.221
MonotonicityNot monotonic
2024-10-04T14:03:20.226150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 171
 
1.7%
1 166
 
1.6%
0.5 161
 
1.6%
0.75 156
 
1.5%
0.6 113
 
1.1%
0.8 101
 
1.0%
0.714 92
 
0.9%
0.833 85
 
0.8%
0.778 69
 
0.7%
0.625 63
 
0.6%
Other values (820) 8950
88.4%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3714 1
< 0.1%
3571 1
< 0.1%
2875 1
< 0.1%
2571 1
< 0.1%
2429 1
< 0.1%
2333 2
< 0.1%
2286 1
< 0.1%
2273 1
< 0.1%
2222 2
< 0.1%
2182 1
< 0.1%

Taxa de Utilização Cartão
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-10-04T14:03:20.669949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2024-10-04T14:03:21.184596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.061 29
 
0.3%
0.045 29
 
0.3%
0.059 28
 
0.3%
0.069 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%

Interactions

2024-10-04T14:03:01.050277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:55.036591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:59.063645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:03.556562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:08.795694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:14.151894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:18.191306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:23.972203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:27.970894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:31.919924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:38.191482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:42.453255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:46.623838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:52.859679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:57.064689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:01.301152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:55.290627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:59.321099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:03.965938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:09.068633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:14.395922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:18.572621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:24.250935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:28.209593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:32.747644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:38.473672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:42.726007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:46.908258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:53.248932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:57.339344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:01.551200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:55.542668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:59.563206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:04.370369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:09.288820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:14.644191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:18.928254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:24.525275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:28.462915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:33.071635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:38.738285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:43.008686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:47.181594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:53.554742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:57.584901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:01.800238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:55.820901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:59.816103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:04.670909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:11.033701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:14.916773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:19.314136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:24.780459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:28.713095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:33.431890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:39.030949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:43.289196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:47.477365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:53.818789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:57.844776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:02.083956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:56.084881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:00.096158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:05.043676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:11.267519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:15.155218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:19.681639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:25.058171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:28.958747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:33.775811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:39.285022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:43.541757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:47.744220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:54.068119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:58.098193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:02.343472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:56.347259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:00.335225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:05.431334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:11.515004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:15.399408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:20.104238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:25.317626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:29.205530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:34.161923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:39.568331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:43.814877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:48.675414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:54.328247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:58.348814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:02.606949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:56.615743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:00.582863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:05.825275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:11.780031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:15.656351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:20.535495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:25.584259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:29.479651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:34.542981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:39.835586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:44.131757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:49.016880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:54.603434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:58.612924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:02.851385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:56.898117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:00.823622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:06.178258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:12.031068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:15.915982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:21.343352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:25.843765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:29.743802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:34.950615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:40.144569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:44.399761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:49.415231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:54.882645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:58.873254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:03.127425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:57.168427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:01.098272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:06.542799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:12.285567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:16.177833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:21.729496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:26.134536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:30.009471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:35.284153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:40.416140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:44.669948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:49.876302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:55.157839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:59.172349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:03.372399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:57.427017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:01.328202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:06.972508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:12.550472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:16.422573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:22.143162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:26.414828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:30.259643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:35.687880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:40.672965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:44.922273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:50.266692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:55.410165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:59.431800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:03.715948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:57.700863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:01.580029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:07.355918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:12.845289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:16.679457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:22.573661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:26.689829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:30.527292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:36.112029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:41.006940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:45.198447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:50.681870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:55.683475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:59.705815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:04.179231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:58.001886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:01.897964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:07.702483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:13.114798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:16.962781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:22.879117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:26.946833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:30.797017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:36.525822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:41.291199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:45.490129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:51.067932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:55.976199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:59.987803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:04.551408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:58.256605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:02.264798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:08.006449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:13.376139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:17.220150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:23.146225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:27.203456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:31.080815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:36.909864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:41.581268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:45.783053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:51.497648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:56.238814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:00.246859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:04.926282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:58.523021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:02.710820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:08.263666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:13.616096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:17.467808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:23.417176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:27.469266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:31.336304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:37.330445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:41.865750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:46.066716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:51.964657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:56.503883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:00.515645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:05.337206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:01:58.786170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:03.148491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:08.548096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:13.891256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:17.856149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:23.684111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:27.726920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:31.653250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:37.801609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:42.165360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:46.359574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:52.426351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:02:56.768496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-04T14:03:00.773924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-04T14:03:21.569422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CLIENTNUMCategoriaCategoria CartãoContatos 12mDependentesEducaçãoEstado CivilFaixa Salarial AnualIdadeInatividade 12mLimiteLimite ConsumidoLimite DisponívelMeses como ClienteMudança Qtde Transações_Q4_Q1Mudanças Transacoes_Q4_Q1Produtos ContratadosQtde Transacoes 12mSexoTaxa de Utilização CartãoValor Transacoes 12m
CLIENTNUM1.0000.0480.0000.011-0.0040.0150.0060.0030.017-0.0080.0140.0030.0110.1110.0160.0240.0140.0060.0140.007-0.002
Categoria0.0481.0000.0000.2390.0210.0250.0170.0280.0240.1960.0320.4020.0190.0190.0560.0600.1660.4580.0360.2410.325
Categoria Cartão0.0000.0001.0000.0100.0180.0000.0280.0530.0210.0000.3350.0190.3370.0130.0000.0000.0670.1090.0840.1490.154
Contatos 12m0.0110.2390.0101.000-0.0410.0000.0070.015-0.0140.0300.023-0.0450.033-0.008-0.093-0.0210.061-0.1680.059-0.059-0.167
Dependentes-0.0040.0210.018-0.0411.0000.0010.0370.043-0.144-0.0090.051-0.0040.054-0.1150.010-0.026-0.0360.0530.000-0.0350.058
Educação0.0150.0250.0000.0000.0011.0000.0110.0170.0140.0000.0000.0070.0000.0020.0080.0130.0000.0040.0110.0000.012
Estado Civil0.0060.0170.0280.0070.0370.0111.0000.0080.0820.0070.0260.0120.0280.0430.0190.0500.0220.0990.0090.0270.104
Faixa Salarial Anual0.0030.0280.0530.0150.0430.0170.0081.0000.0840.0170.2780.0220.2780.0460.0170.0200.0070.0560.8390.1650.093
Idade0.0170.0240.021-0.014-0.1440.0140.0820.0841.0000.0440.0020.014-0.0020.769-0.040-0.070-0.014-0.0540.0000.011-0.039
Inatividade 12m-0.0080.1960.0000.030-0.0090.0000.0070.0170.0441.000-0.028-0.043-0.0160.057-0.046-0.019-0.007-0.0510.019-0.027-0.032
Limite0.0140.0320.3350.0230.0510.0000.0260.2780.002-0.0281.0000.1310.9310.007-0.0120.021-0.0590.0340.439-0.4170.028
Limite Consumido0.0030.4020.019-0.045-0.0040.0070.0120.0220.014-0.0430.1311.000-0.1540.0060.0780.0360.0120.0400.0330.7090.018
Limite Disponível0.0110.0190.3370.0330.0540.0000.0280.278-0.002-0.0160.931-0.1541.0000.008-0.0400.007-0.0710.0220.440-0.6860.022
Meses como Cliente0.1110.0190.013-0.008-0.1150.0020.0430.0460.7690.0570.0070.0060.0081.000-0.034-0.054-0.014-0.0390.011-0.004-0.029
Mudança Qtde Transações_Q4_Q10.0160.0560.000-0.0930.0100.0080.0190.017-0.040-0.046-0.0120.078-0.040-0.0341.0000.3010.0240.2350.0180.0940.224
Mudanças Transacoes_Q4_Q10.0240.0600.000-0.021-0.0260.0130.0500.020-0.070-0.0190.0210.0360.007-0.0540.3011.0000.0260.0860.0450.0320.135
Produtos Contratados0.0140.1660.0670.061-0.0360.0000.0220.007-0.014-0.007-0.0590.012-0.071-0.0140.0240.0261.000-0.2270.0000.065-0.279
Qtde Transacoes 12m0.0060.4580.109-0.1680.0530.0040.0990.056-0.054-0.0510.0340.0400.022-0.0390.2350.086-0.2271.0000.1630.0400.880
Sexo0.0140.0360.0840.0590.0000.0110.0090.8390.0000.0190.4390.0330.4400.0110.0180.0450.0000.1631.0000.2790.247
Taxa de Utilização Cartão0.0070.2410.149-0.059-0.0350.0000.0270.1650.011-0.027-0.4170.709-0.686-0.0040.0940.0320.0650.0400.2791.0000.019
Valor Transacoes 12m-0.0020.3250.154-0.1670.0580.0120.1040.093-0.039-0.0320.0280.0180.022-0.0290.2240.135-0.2790.8800.2470.0191.000

Missing values

2024-10-04T14:03:05.891016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-04T14:03:07.070428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMCategoriaIdadeSexoDependentesEducaçãoEstado CivilFaixa Salarial AnualCategoria CartãoMeses como ClienteProdutos ContratadosInatividade 12mContatos 12mLimiteLimite ConsumidoLimite DisponívelMudanças Transacoes_Q4_Q1Valor Transacoes 12mQtde Transacoes 12mMudança Qtde Transações_Q4_Q1Taxa de Utilização Cartão
0768805383Cliente45M3Ensino MédioCasado$60K - $80KBlue3951312691.077711914.01335.01144421625.0000.061
1818770008Cliente49F5Ensino SuperiorSolteiroLess than $40KBlue446128256.08647392.01541.01291333714.0000.105
2713982108Cliente51M3Ensino SuperiorCasado$80K - $120KBlue364103418.003418.02594.01887202333.0000.000
3769911858Cliente40F4Ensino MédioNão informadoLess than $40KBlue343413313.02517796.01405.01171202333.0000.760
4709106358Cliente40M3Sem ensino formalCasado$60K - $80KBlue215104716.004716.02175.0816282.5000.000
5713061558Cliente44M2Ensino SuperiorCasado$40K - $60KBlue363124010.012472763.01376.01088240.8460.311
6810347208Cliente51M4Não informadoCasado$120K +Gold4661334516.0226432252.01975.01330310.7220.066
7818906208Cliente32M0Ensino MédioNão informado$60K - $80KSilver2722229081.0139627685.02204.01538360.7140.048
8710930508Cliente37M3Sem ensino formalSolteiro$60K - $80KBlue3652022352.0251719835.03355.01350241182.0000.113
9719661558Cliente48M2Ensino SuperiorSolteiro$80K - $120KBlue3663311656.016779979.01524.01441320.8820.144
CLIENTNUMCategoriaIdadeSexoDependentesEducaçãoEstado CivilFaixa Salarial AnualCategoria CartãoMeses como ClienteProdutos ContratadosInatividade 12mContatos 12mLimiteLimite ConsumidoLimite DisponívelMudanças Transacoes_Q4_Q1Valor Transacoes 12mQtde Transacoes 12mMudança Qtde Transações_Q4_Q1Taxa de Utilização Cartão
10117712503408Cliente57M2Ensino SuperiorCasado$80K - $120KBlue4063417925.0190916016.00.712174981110.8200.106
10118713755458Cancelado50M1Não informadoNão informado$80K - $120KBlue366349959.09529007.00.82510310631.1000.096
10119716893683Cancelado55F3Sem ensino formalSolteiroNão informadoBlue4743314657.0251712140.00.1666009530.5140.172
10120710841183Cliente54M1Ensino MédioSolteiro$60K - $80KBlue3452013940.0210911831.00.660155771140.7540.151
10121713899383Cliente56F1Ensino SuperiorSolteiroLess than $40KBlue504143688.06063082.00.570145961200.7910.164
10122772366833Cliente50M2Ensino SuperiorSolteiro$40K - $60KBlue403234003.018512152.00.703154761170.8570.462
10123710638233Cancelado41M2Não informadoDivorciado$40K - $60KBlue254234277.021862091.00.8048764690.6830.511
10124716506083Cancelado44F1Ensino MédioCasadoLess than $40KBlue365345409.005409.00.81910291600.8180.000
10125717406983Cancelado30M2Ensino SuperiorNão informado$40K - $60KBlue364335281.005281.00.5358395620.7220.000
10126714337233Cancelado43F2Ensino SuperiorCasadoLess than $40KSilver2562410388.019618427.00.70310294610.6490.189